Aurelia Butler

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    in Computers Math

    Understanding potential topological quantum bits

    Quantum computers promise great advances in many fields — from cryptography to the simulation of protein folding. Yet, which physical system works best to build the underlying quantum bits is still an open question. Unlike regular bits in your computer, these so-called qubits cannot only take the values 0 and 1, but also mixtures of the two. While this potentially makes them very useful, they also become very unstable.
    One approach to solve this problem bets on topological qubits that encode the information in their spatial arrangement. That could provide a more stable and error-resistant basis for computation than other setups. The problem is that no one has ever definitely found a topological qubit yet.
    An international team of researchers from Austria, Copenhagen, and Madrid around Marco Valentini from the Nanoelectronics group at IST Austria now have examined a setup which was predicted to produce the so-called Majorana zero modes — the core ingredient for a topological qubit. They found that a valid signal for such modes can in fact be a false flag.
    Half of an Electron
    The experimental setup is composed of a tiny wire just some hundred nanometers — some millionths of a millimeter — long, grown by Peter Krogstrup from Microsoft Quantum and University of Copenhagen. These appropriately-called nanowires form a free-floating connection between two metal conductors on a chip. They are coated with a superconducting material that loses all electrical resistance at very low temperatures. The coating goes all the way up to a tiny part left at one end of the wire, which forms a crucial part of the setup: the junction. The whole contraption is then exposed to a magnetic field.
    The scientists’ theories predicted that Majorana zero modes — the basis for the topological qubit they were looking for — should appear in the nanowire. These Majorana zero modes are a strange phenomenon, because they started out as a mathematical trick to describe one electron in the wire as composed of two halves. Usually, physicists do not think of electrons as something that can be split, but using this nanowire setup it should have been possible so separate these “half-electrons” and to use them as qubits.
    “We were excited to work on this very promising material platform,” explains Marco Valentini, who joined IST Austria as an intern before becoming a PhD student in the Nanoelectronics group. “What we expected to see was the signal of Majorana zero modes in the nanowire, but we found nothing. First, we were confused, then frustrated. Eventually, and in close collaboration with our colleagues from the Theory of Quantum Materials and Solid State Quantum Technologies group in Madrid, we examined the setup, and found out what was wrong with it.”
    A False Flag
    After attempting to find the signatures of the Majorana zero modes, the researchers began to vary the nanowire setup to check whether any effects from its architecture were disturbing their experiment. “We did several experiments on different setups to find out what was going wrong,” Valentini explains. “It took us a while, but when we doubled the length of the uncoated junction from a hundred nanometers to two hundred, we found our culprit.”
    When the junction was big enough the following happened: The exposed inner nanowire formed a so-called quantum dot — a tiny speck of matter that shows special quantum mechanical properties due to its confined geometry. The electrons in this quantum dot could then interact with the ones in the coating superconductor next to it, and by that mimic the signal of the “half-electrons” — the Majorana zero modes — which the scientists were looking for.
    “This unexpected conclusion came after we established the theoretical model of how the quantum dot interacts with the superconductor in a magnetic field and compared the experimental data with detailed simulations performed by Fernando Peñaranda, a PhD student in the Madrid team,” says Valentini.
    “Mistaking this mimicking signal for a Majorana zero mode shows us how careful we have to be in our experiments and in our conclusions,” Valentini cautions. “While this may seem like a step back in the search for Majorana zero modes, it actually is a crucial step forward in understanding nanowires and their experimental signals. This finding shows that the cycle of discovery and critical examination among international peers is central to the advancement of scientific knowledge.” More

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    in Computers Math

    Using AI to predict 3D printing processes

    Additive manufacturing has the potential to allow one to create parts or products on demand in manufacturing, automotive engineering, and even in outer space. However, it’s a challenge to know in advance how a 3D printed object will perform, now and in the future.
    Physical experiments — especially for metal additive manufacturing (AM) — are slow and costly. Even modeling these systems computationally is expensive and time-consuming.
    “The problem is multi-phase and involves gas, liquids, solids, and phase transitions between them,” said University of Illinois Ph.D. student Qiming Zhu. “Additive manufacturing also has a wide range of spatial and temporal scales. This has led to large gaps between the physics that happens on the small scale and the real product.”
    Zhu, Zeliang Liu (a software engineer at Apple), and Jinhui Yan (professor of Civil and Environmental Engineering at the University of Illinois), are trying to address these challenges using machine learning. They are using deep learning and neural networks to predict the outcomes of complex processes involved in additive manufacturing.
    “We want to establish the relationship between processing, structure, properties, and performance,” Zhu said.
    Current neural network models need large amounts of data for training. But in the additive manufacturing field, obtaining high-fidelity data is difficult, according to Zhu. To reduce the need for data, Zhu and Yan are pursuing ‘physics informed neural networking,’ or PINN. More

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    in Computers Math

    Novel microscopy method provides look into future of cell biology

    What if a microscope allowed us to explore the 3D microcosm of blood vessels, nerves, and cancer cells instantaneously in virtual reality? What if it could provide views from multiple directions in real time without physically moving the specimen and worked up to 100 times faster than current technology?
    UT Southwestern scientists collaborated with colleagues in England and Australia to build and test a novel optical device that converts commonly used microscopes into multiangle projection imaging systems. The invention, described in an article in today’s Nature Methods, could open new avenues in advanced microscopy, the researchers say.
    “It is a completely new technology, although the theoretical foundations for it can be found in old computer science literature,” says corresponding author Reto Fiolka, Ph.D. Both he and co-author Kevin Dean, Ph.D., are assistant professors of cell biology and in the Lyda Hill Department of Bioinformatics at UT Southwestern.
    “It is as if you are holding the biological specimen with your hand, rotating it, and inspecting it, which is an incredibly intuitive way to interact with a sample. By rapidly imaging the sample from two different perspectives, we can interactively visualize the sample in virtual reality on the fly,” says Dean, director of the UTSW Microscopy Innovation Laboratory, which collaborates with researchers across campus to develop custom instruments that leverage advances in light microscopy.
    Currently, acquiring 3D-image information from a microscope requires a data-intensive process, in which hundreds of 2D images of the specimen are assembled into a so-called image stack. To visualize the data, the image stack is then loaded into a graphics software program that performs computations to form two-dimensional projections from different viewing perspectives on a computer screen, the researchers explain.
    “Those two steps require a lot of time and may need a very powerful and expensive computer to interact with the data,” Fiolka says. More

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    in Computers Math

    New data science platform speeds up Python queries

    Researchers from Brown University and MIT have developed a new data science framework that allows users to process data with the programming language Python — without paying the “performance tax” normally associated with a user-friendly language.
    The new framework, called Tuplex, is able to process data queries written in Python up to 90 times faster than industry-standard data systems like Apache Spark or Dask. The research team unveiled the system in research presented at SIGMOD 2021, a premier data processing conference, and have made the software freely available to all.
    “Python is the primary programming language used by people doing data science,” said Malte Schwarzkopf, an assistant professor of computer science at Brown and one of the developers of Tuplex. “That makes a lot of sense. Python is widely taught in universities, and it’s an easy language to get started with. But when it comes to data science, there’s a huge performance tax associated with Python because platforms can’t process Python efficiently on the back end.”
    Platforms like Spark perform data analytics by distributing tasks across multiple processor cores or machines in a data center. That parallel processing allows users to deal with giant data sets that would choke a single computer to death. Users interact with these platforms by inputting their own queries, which contain custom logic written as “user-defined functions” or UDFs. UDFs specify custom logic, like extracting the number of bedrooms from the text of a real estate listing for a query that searches all of the real estate listings in the U.S. and selects all the ones with three bedrooms.
    Because of its simplicity, Python is the language of choice for creating UDFs in the data science community. In fact, the Tuplex team cites a recent poll showing that 66% of data platform users utilize Python as their primary language. The problem is that analytics platforms have trouble dealing with those bits of Python code efficiently.
    Data platforms are written in high-level computer languages that are compiled before running. Compilers are programs that take computer language and turn it into machine code — sets of instructions that a computer processor can quickly execute. Python, however, is not compiled beforehand. Instead, computers interpret Python code line by line while the program runs, which can mean far slower performance. More

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    in Computers Math

    How children integrate information

    Children learn a huge number of words in the early preschool years. A two-year-old might be able to say just a handful of words, while a five-year-old is quite likely to know many thousands. How do children achieve this marvelous feat? The question has occupied psychologists for over a century: In countless carefully designed experiments, researchers titrate the information children use to learn new words. How children integrate different types of information, has remained unclear.
    “We know that children use a lot of different information sources in their social environment, including their own knowledge, to learn new words. But the picture that emerges from the existing research is that children have a bag of tricks that they can use,” says Manuel Bohn, a researcher at the Max Planck Institute for Evolutionary Anthropology.
    For example, if you show a child an object they already know — say a cup — as well as an object they have never seen before, the child will usually think that a word they never heard before belongs with the new object. Why? Children use information in the form of their existing knowledge of words (the thing you drink out of is called a “cup”) to infer that the object that doesn’t have a name goes with the name that doesn’t have an object. Other information comes from the social context: children remember past interactions with a speaker to find out what they are likely to talk about next.
    “But in the real world, children learn words in complex social settings in which more than just one type of information is available. They have to use their knowledge of words while interacting with a speaker. Word learning always requires integrating multiple, different information sources,” Bohn continues. An open question is how children combine different, sometimes even conflicting, sources of information.
    Predictions by a computer program
    In a new study, a team of researchers from the Max Planck Institute for Evolutionary Anthropology, MIT, and Stanford University takes on this issue. In a first step, they conducted a series of experiments to measure children’s sensitivity to different information sources. Next, they formulated a computational cognitive model which details the way that this information is integrated. More

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    in Heart

    Focusing on Asian giant hornets distorts the view of invasive species

    Fingers crossed for finding nothing: July marks the main trapping season to check for Asian giant hornets still infesting Washington state.

    The first of these invasive hornets found in North America in 2021, in June, was probably not from a nest made this year, scientists say. So that find doesn’t say how well, or if, the pests might have survived the winter. Yet that hornet shows quite well the relentless risk of newly arriving insects.

    That initial specimen, a “crispy” dead male insect lying on a lawn in Marysville, Wash., belongs to the hefty species Vespa mandarinia. Nicknamed murder hornets, these were detected flying loose in Canada for the first time in 2019 and in the United States in 2020 (SN: 5/29/20). Yet the “dry, crispy” male is not part of known hornet invasions, said entomologist Sven Spichiger at a news conference on June 16.

    Testing shows the male “is definitely not the same genetic line as the ones we have found,” said Spichiger, of the Washington State Department of Agriculture in Olympia. Neither the U.S. finds, until now all from Washington’s Whatcom County, nor British Columbia’s on the other side of the border are closely related to the newfound hornet. It’s a separate incursion no one had noticed until now.

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    This oddball new specimen may help correct the skewed impression that sneaky invasive arrivals are rare. The hornets’ appearance in North America may have been a shock to some, but in reality, worrisome insects show up often, and will probably keep doing so. Fortunately making a permanent home is harder than getting here, scientists say.

    When news of the Asian giant hornets’ arrival first broke in 2019, one of the people who was not at all surprised at a foreign species was entomologist Doug Yanega of the University of California, Riverside. “It is very fair to say that there are many invasive species,” he emphasizes. “We just got a new African mantis species in California this past year in LA, and the expectation is that it is likely to spread.”

    But even alarming pest arrivals rarely kick up the fuss prompted by Asian giant hornets. At a peak in hornet news during May 2020, Yanega contrasted the new intruders with the South American palm weevil (Rhynchophorus palmarum). That big weevil had reached southern California and could “wipe out every palm tree in the state,” according to Yanega. Yet, “there have been ZERO [national] mainstream media reports about this, an insect that seriously threatens to have a VASTLY greater negative impact on the economy and our way of life than those hornets ever will,” he fumed in an e-mail.

    That relentless influx of invading insects may be one reason so few make it into the general news. For instance, U.S. Customs and Border Protection reported 31,785 incidents detecting some pest just for fiscal year 2020.

    For 2021, to pick just one example of worrisome arrivals that have not gone viral, inspectors at Washington Dulles International Airport in Virginia and later at Baltimore/Washington International noticed little brown pests called Khapra beetles (Trogoderma granarium) in Basmati rice and then in dried cow peas that travelers were trying to bring in from abroad. Officials banned the contaminated foodstuffs.

    The Dulles contraband had the bigger number of living insects: 12 larvae and four adults. Even that tiny number of tiny insects was unacceptable. This is the only insect species that U.S. customs officials act upon even when all specimens are found dead. The beetles nibble stored seeds but will also soil the goods with stray body parts and hairs that can make human babies fed dirty grain quite sick and adults uncomfortable. In 1953, a major California effort started to stamp out infestations of Khapra beetles and eventually preserved crop marketability. But the effort was expensive, costing the equivalent of about $90 million in today’s economy.

    Tiny but destructive Khapra beetles (shown, side and front view), which California eliminated at great expense, almost got into the United States at least twice in 2021 in air passenger luggage. Customs stopped those two incursions.Both: Pest and Diseases Image Library, Bugwood.org (CC BY-NC 3.0 US)

    Tiny but destructive Khapra beetles (shown, side and front view), which California eliminated at great expense, almost got into the United States at least twice in 2021 in air passenger luggage. Customs stopped those two incursions.Both: Pest and Diseases Image Library, Bugwood.org (CC BY-NC 3.0 US)

    Beetles aside, menacing hornets of other species have shown up before the latest Asian giants, says Paul van Westendorp, an apiculture specialist who now strategizes British Columbia’s fight against V. mandarinia. In May 2019, just months before the discovery of an Asian giant hornet’s arrival, a V. soror hornet appeared in Canada. It was “alive, but not for long,” van Westendorp says. “I had a chance to admire that specimen.” Not a frail beast, this species hunts down other insects and has been reported to catch prey as large as a gecko. V. soror looks very much like a V. mandarinia, he says.

    Even Asian giant hornets themselves have turned up at least once in the United States before 2020. An inspector in 2016 flagged a package coming into the San Francisco airport holding a papery insect nest but not mentioning insects on the label. The nest held Asian giant hornet larvae and pupae, some still alive when discovered. These and other species of hornets, including the ominously named V. bellicosa, accounted for about half of the 50 interceptions of hornets and yellow jackets flagged from 2010 to 2018 at U.S. ports of entry, researchers reported in 2020 in Insect Systematics and Diversity.  

    Only some stowaways will manage to make permanent homes in new territory. Of these, the real troublemakers seem to be a minority. For instance, out of 455 plant-attacking insects that settled into forests in the continental United States, 62 cause noticeable damage, according to a 2011 tally from U.S. Forest Service researcher Juliann Aukema and colleagues. Even a few rampaging invasive pests, though, can get expensive. Biologists are throwing themselves into the fight.

    Relentless as the onslaught of unwanted arrivals is, there’s hope for stamping out the more noticeable invasions if caught early. Vespa hornets are “very large-bodied and obvious, so people will see them,” says entomologist Lynn Kimsey of the University of California, Davis, one of the authors of the 2020 hornet overview. A Vespa affinis nest showed up in San Pedro, in Southern California at least a decade ago. However, she says, “it was killed and there’s been no sighting of the species since, as far as I’ve heard.”

    Catching such intrusions early isn’t always easy, however. The port of Oakland takes in about 1 million shipping containers from overseas a year, but at best U.S. Department of Agriculture inspectors can check maybe only 10 percent for stowaway insects, Kimsey says. Add to this all the cargo coming into Long Beach, San Diego and the other West Coast ports — plus all the cargo jets. “What’s amazing is that we don’t see more invasives,” she says. “I think this tells you how hard it is for exotic species to get established.”

    They’ll keep arriving though. All the more reason to keep an eye out for something funny on the lawn, even if it’s just a withered nugget. More

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    in Computers Math

    Decoding electron dynamics

    Electron motion in atoms and molecules is of fundamental importance to many physical, biological, and chemical processes. Exploring electron dynamics within atoms and molecules is essential for understanding and manipulating these phenomena. Pump-probe spectroscopy is the conventional technique. The 1999 Nobel Prize in Chemistry provides a well-known example wherein femtosecond pumped laser pulses served to probe the atomic motion involved in chemical reactions. However, because the timescale of electron motion within atoms and molecules is on the order of attoseconds (10-18 seconds) rather than femtoseconds (10-15 seconds), attosecond pulses are required to probe electron motion. With the development of the attosecond technology, lasers with pulse durations shorter than 100 attoseconds have become available, providing opportunities for probing and manipulating electron dynamics in atoms and molecules.
    Another important method for probing electron dynamics is based on strong-field tunneling ionization. In this method, a strong femtosecond laser is employed to induce tunneling ionization, a quantum mechanical phenomenon that causes electrons to tunnel through the potential barrier and escape from the atom or molecule. This process provides photoelectron-encoded information about ultrafast electron dynamics. Based on the relationship between the ionization time and the final momentum of the tunneling ionized photoelectron, electron dynamics can be observed with attosecond-scale resolution.
    The relationship between ionization time and the final momentum of the tunneling photoelectron has been theoretically established in terms of a “quantum orbit” model and the accuracy of the relationship has been verified experimentally. But which quantum orbits contribute to the photoelectron yield in strong-field tunneling ionization has remained a mystery, as well as how different orbits correspond differently to momentum and ionization times. So, identifying the quantum orbits is vital to the study of ultrafast dynamic processes using tunneling ionization.
    As reported in Advanced Photonics, researchers at Huazhong University of Science and Technology (HUST) proposed a scheme to identify and weigh the quantum orbits in strong-field tunneling ionization. In their scheme, a second harmonic (SH) frequency is introduced to perturb the tunneling ionization process. The perturbation SH is much weaker than the fundamental field, so it does not change the final momentum of the electron that is tunneling toward ionization. However, it can significantly alter the photoelectron yield, due to the highly nonlinear nature of tunneling ionization. Because of different ionization times, different quantum orbitals have different responses to the intervening SH field. By changing the phase of the SH field relative to the fundamental driving field and monitoring the responses of the photoelectron yield, the quantum orbits of tunneling ionized electrons can be accurately identified. Based on this scheme, the mysteries of the so-called “long” and “short” quantum orbits in strong-field tunneling ionization can be resolved, and their relative contribution to the photoelectron yield at each momentum is able to be accurately weighted. This is a very important development for the application of strong-field tunneling ionization as a method of photoelectron spectroscopy.
    A collaborative team effort led by HUST graduate students Jia Tan, under the supervision of Professor Yueming Zhou, along with Shengliang Xu and Xu Han, under the supervision of Professor Qingbin Zhang, the study indicates that the hologram generated by the multi-orbit contribution from the photoelectronic spectrum can provide valuable information regarding the phase of the tunneled electron. Its wave packet encodes rich information about atomic and molecular electron dynamics. According to Peixiang Lu, HUST professor, vice director of the Wuhan National Laboratory for Optoelectronics, and senior author of the study, “Attosecond temporal and subangstrom spatial resolution measurement of electron dynamics is made possible by this new scheme for resolving and weighing quantum orbits.”
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    Machine learning helps in predicting when immunotherapy will be effective

    When it comes to defense, the body relies on attack thanks to the lymphatic and immune systems. The immune system is like the body’s own personal police force as it hunts down and eliminates pathogenic villains.
    “The body’s immune system is very good at identifying cells that are acting strangely. These include cells that could develop into tumors or cancer in the future,” says Federica Eduati from the department of Biomedical Engineering at TU/e. “Once detected, the immune system strikes and kills the cells.”
    Stopping the attack
    But it’s not always so straightforward as tumor cells can develop ways to hide themselves from the immune system.
    “Unfortunately, tumor cells can block the natural immune response. Proteins on the surface of a tumor cell can turn off the immune cells and effectively put them in sleep mode,” says Oscar Lapuente-Santana, PhD researcher in the Computational Biology group.
    Fortunately, there is a way to wake up the immune cells and restore their antitumor immunity, and it’s based on immunotherapy. More